| ### HaT5(T5-base) | |
| This is a fine-tuned model of T5 (base) on the hate speech detection dataset. It is intended to be used as a classification model for identifying Tweets (0 - HOF(hate/offensive); 1 - NOT). The task prefix we used for the T5 model is 'classification: '. | |
| More information about the original pre-trained model can be found [here](https://huggingface.co/t5-base) | |
| Classification examples: | |
| |Prediction|Tweet| | |
| |-----|--------| | |
| |0 |Why the fuck I got over 1000 views on my story 😂😂 nothing new over here | | |
| |1. |first of all there is no vaccine to cure , whthr it is capsules, tablets, or injections, they just support to fight with d virus. I do not support people taking any kind of home remedies n making fun of an ayurvedic medicine..😐 | | |
| # More Details | |
| For more details about the datasets and eval results, see [our paper for this work here](https://arxiv.org/abs/2202.05690) | |
| The paper was accepted at the International Joint Conference on Neural Networks (IJCNN) conference 2022. | |
| # How to use | |
| ```python | |
| from transformers import T5ForConditionalGeneration, T5Tokenizer | |
| import torch | |
| model = T5ForConditionalGeneration.from_pretrained("sana-ngu/HaT5") | |
| tokenizer = T5Tokenizer.from_pretrained("t5-base") | |
| tokenizer.pad_token = tokenizer.eos_token | |
| input_ids = tokenizer("Old lions in the wild lay down and die with dignity when they can't hunt anymore. If a government is having 'teething problems' handling aid supplies one full year into a pandemic, maybe it should take a cue and get the fuck out of the way? ", padding=True, truncation=True, return_tensors='pt').input_ids | |
| outputs = model.generate(input_ids) | |
| pred = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| print(pred) | |
| ``` | |